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GPSRO Error Characterization: Analysis of RO measurement errors based on SAC-C radio occultation data recorded in open-loop and closed-loop mode. Martin S Lohmann. This presentation. CDAAC (COSMIC) processing overview Summary of error estimation techniques

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GPSRO Error Characterization: Analysis of RO measurement errors based on SAC-C radio occultation data recorded in open-loop and closed-loop mode.

Martin S Lohmann

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This presentation

  • CDAAC (COSMIC) processing overview

  • Summary of error estimation techniques

    • Stratosphere upper troposphere (GO-region)

    • Mid and lower troposphere (RH)

  • SAC-C open-loop and closed loop error characteristics

    • Examples of error profiles

    • Error statistics and comparison with ECMWF analysis

    • Error distributions

    • Error correlation lengths

  • Summary slide

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Radio occultation principle

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Open loop vs. Closed Loop - outline

  • GPS radio occultation signals can be tracked using either traditional so-called closed loop/phase locked loop (PLL) tracking or so-called open loop tracking (OL)

  • PLL is based on a feed-back from the tracked signal itself which may result in tracking errors. PLL is performing (almost) optimal noise filtering when tracking correctly

    Severe tracking errors are common in GPS-MET, CHAMP and SAC-C RO data particularly in the lower troposphere. Tracking errors require sophisticated QC procedures to remove

  • OL tracking does not involve any feed-back mechanisms. Consequently, OL RO are not affected by tracking errors - but can be more noisy

  • COSMIC and SAC-C (in open loop mode) switch to OL tracking below approx. 10 km

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CDAAC RO processing - overview

Occultation processed by CDAAC can be divided into two separate

regions where different data processing strategies are applied:

  • GO – region: processing is based on geometrical optics (GO). Bending angle and refractivity profiles are retrieved from the GPS L1 and L2 signal phases. Statistical optimization (use of climatology) is applied in the retrieval of refractivities. Data are smoothed over approx. 1 km

    This region covers the height range from the top of the occultation to the lowest height for which the L2 signal is being tracked

  • RH – region: processing is based on radio holographics (RH) and bending angle and refractivity profiles are retrieved from the L1 phase and amplitude using FSI

    The RH region extends from the lowest height to which the L2 signal can be tracked down to the lowest point in an occultation. Data are smoothed over approx. 100 m

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RO measurement errors

GO Region:

  • Climatology used for retrieval of refractivity

  • Background/ionospheric noise

  • Tracking errors (closed loop)

    RH Region:

  • Early signal truncation (closed loop)

  • Background/ionospheric noise

  • Small scale horizontal variations

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Error estimation techniques - outline

  • GO region: CDAAC (COSMIC) error estimation is based on (Lohmann 2005):

    Below the E-layer the magnitude and structure of the bending angle measurement errors are fairly uniform [Kursinski et al., 1997; Kuo et al., 2004] Consequently, the bending angle errors can be estimated from high altitude differences between a climatology and the observations

    Errors in the climatology used for SO are estimated from differences between measurements and climatology in the lower part of the GO region

  • RH region: CDAAC (COSMIC) error estimation is based on (Lohmann 2006):

    Measurement errors are estimated by mapping fluctuations in the FSI-amplitude to bending angle errors

    In the lower troposphere where the RO signals are very noisy, an alternative technique is applied where small scale bending angle fluctuations are considered as errors

Sac c occultation april 30 2005 14 34 utc 24 s 82 w l.jpg

SAC-C occultation, April 30, 2005, 14.34 UTC, 24S- 82W.

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Estimated errors vs. SAC-C - ECMWF differencesData from March 16 to May 16 2005

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Error distributions low latitudes

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Error distributions mid latitudes

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Error distributions high latitudes

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Error autocorrelation functions (1)

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Error autocorrelation functions (2)

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Title: JCSDA GPSRO assimilation

Purpose: GPSROerror characterization

Progress so far:

  • Implementation of dynamic error estimation (will be included in the next CDAAC operational update)

  • Extensive analysis of GPSRO measurement error characteristics (error profiles, error distributions, and error correlation lengths)

    Future plans:

  • Post launch fine-tuning of QC and data-processing

  • Better understanding of model minus observation differences

  • Investigating the possibility of filtering out gravity waves and other atmospheric structures which are not included in NWP model fields

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